{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0da7e36",
   "metadata": {},
   "source": [
    "# Additional Figure: Cygnus A 90% C.L. flux upper limit\n",
    "This figure is provided as additional material and is not published in the paper \"Evidence for Neutrino Emission from X-ray Bright Active Galactic Nuclei with IceCube\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b1e3f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'legend.fontsize': 'large',\n",
    "    'axes.labelsize': 'xx-large',\n",
    "    'xtick.labelsize':'xx-large',\n",
    "    'ytick.labelsize':'xx-large',\n",
    "    'figure.figsize':(4,3.3)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a745a04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv('CygnusA_Erange_and_UL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18abe6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = np.logspace(np.log10(data['Erange_min']), np.log10(data['Erange_max']), 100)\n",
    "ax.plot(x, data['UL'].values*1e3 * (x/1e3)**(-1.6+2), lw=2, label=r\"UL $\\gamma=1.6$ ($\\gamma_{\\mathrm{fix}}$)\")\n",
    "\n",
    "ax.loglog()\n",
    "\n",
    "ax.set_xlim(2e4, 5e6)\n",
    "ax.set_ylim(1e-13, 1e-11)\n",
    "\n",
    "ax.set_xlabel(\"Neutrino energy [GeV]\")\n",
    "ax.set_ylabel(r\"E$^2\\phi$ TeV/cm$^2$/s\")\n",
    "\n",
    "ax.legend(frameon=False, loc='upper right')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "022f05c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Publications",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
